{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from pathlib import Path\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "import re\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/deepseek_chat_res_0310.json\n",
      "results/deepseek_chat_res_0310-5.json\n",
      "results/deepseek_chat_res_0310-4.json\n",
      "results/deepseek_chat_res_0310-3.json\n",
      "results/deepseek_chat_res_0310-2.json\n",
      "results/deepseek_chat_res_0304.json\n",
      "results/deepseek_chat_res_0316china_trad_chinese.json\n",
      "results/deepseek_chat_res_0316china_simp_chinese.json\n",
      "results/deepseek_chat_res_0310-6.json\n",
      "(1741, 103)\n"
     ]
    }
   ],
   "source": [
    "deepseek_chat_res = pd.DataFrame()\n",
    "for file_name in glob('results/deepseek_chat*.json'):\n",
    "    print(file_name)\n",
    "    with open(file_name, \"r\", encoding=\"utf-8\") as json_file:\n",
    "        data = json.load(json_file)\n",
    "        data = pd.DataFrame(data)\n",
    "        deepseek_chat_res = pd.concat([deepseek_chat_res, data], axis=0, ignore_index=True)\n",
    "\n",
    "# deepseek_chat_res = pd.DataFrame(deepseek_chat_res)\n",
    "print(deepseek_chat_res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/openai_gpt4o_res_0310.json\n",
      "results/openai_gpt4o_res_0304.json\n",
      "(8100, 103)\n"
     ]
    }
   ],
   "source": [
    "openai_gpt4o_res = []\n",
    "for file_name in glob('results/openai_gpt4o*.json'):\n",
    "    print(file_name)\n",
    "    with open(file_name, \"r\", encoding=\"utf-8\") as json_file:\n",
    "        openai_gpt4o_res.extend(json.load(json_file))\n",
    "\n",
    "openai_gpt4o_res = pd.DataFrame(openai_gpt4o_res)\n",
    "print(openai_gpt4o_res.shape)\n",
    "# openai_gpt4o_values = np.array([list(res.values()) for res in openai_gpt4o_res])\n",
    "# print(openai_gpt4o_values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/mistral_batch_output6b6a1db8-d384-4d59-99c2-ea08684a02cd_clean.json\n",
      "results/mistral_batch_outputd6b9e0db-c1f1-4dd0-9e00-a4aa2d2ee151_clean.json\n",
      "results/mistral_batch_output0daa20b4-b7b7-4870-b854-c9068591102b_clean.json\n",
      "results/mistral_batch_outputfefc934d-77f4-4703-9c78-3f1005604106_clean.json\n",
      "results/mistral_batch_outputa31d44bf-0a92-4f30-a79a-93f77c9461fa_clean.json\n",
      "results/mistral_batch_output5d12d3df-6f2c-428d-b0e1-357d98328f4b_clean.json\n",
      "results/mistral_batch_outputfc1150e4-57ee-4ae8-bd22-658e4bee9575_clean.json\n",
      "results/mistral_batch_output293b6e76-de61-4638-8654-96f0c52a351a_clean.json\n",
      "results/mistral_batch_outputc7070dde-f94d-4e6b-8885-9360eac78111_clean.json\n",
      "results/mistral_batch_outputbba0c948-64df-4715-8b3c-d081e710a74d_clean.json\n",
      "results/mistral_batch_outputa7210a65-f794-4269-a34f-e27d3730a194_clean.json\n",
      "(10014, 79)\n"
     ]
    }
   ],
   "source": [
    "mistral_res = []\n",
    "for file_name in glob('results/mistral*_clean.json'):\n",
    "    print(file_name)\n",
    "\n",
    "    with open(file_name, \"r\", encoding=\"utf-8\") as json_file:\n",
    "        mistral_res.extend(json.load(json_file))\n",
    "    \n",
    "mistral_res = pd.DataFrame(mistral_res)\n",
    "print(mistral_res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/xai_grok2_res_0311-2.json\n",
      "results/xai_grok2_res_0311-3.json\n",
      "results/xai_grok2_res_0310.json\n",
      "results/xai_grok2_res_0311.json\n",
      "(1401, 102)\n"
     ]
    }
   ],
   "source": [
    "xai_grok_res = []\n",
    "for file_name in glob('results/xai_grok*.json'):\n",
    "    print(file_name)\n",
    "    with open(file_name, \"r\", encoding=\"utf-8\") as json_file:\n",
    "        xai_grok_res.extend(json.load(json_file))\n",
    "       \n",
    "xai_grok_res = pd.DataFrame(xai_grok_res)\n",
    "print(xai_grok_res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "RQs = list(deepseek_chat_res.columns)\n",
    "# RQs.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_average_scores(responses_df, RQs):\n",
    "    # Filter only existing columns\n",
    "    existing_RQs = [rq for rq in RQs if rq in responses_df.columns]\n",
    "    \n",
    "    if not existing_RQs:\n",
    "        return {}  # Return empty dict if no valid RQs are found\n",
    "\n",
    "    responses = responses_df[existing_RQs]  # Select only available columns\n",
    "    values = np.array(responses, dtype=np.float64)  # Ensure proper dtype for calculations\n",
    "    avg = np.nanmean(values, axis=0)  # Compute mean while ignoring NaNs\n",
    "    mu = {k: v for k, v in zip(existing_RQs, avg)}\n",
    "    \n",
    "    \n",
    "    std_dev = np.nanstd(data, ddof=1, axis=0)  # ddof=1 for sample standard deviation\n",
    "    sig = {k: v for k, v in zip(existing_RQs, std_dev)}\n",
    "    \n",
    "    return [mu, sig]   # Create dictionary of averages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_rq_values(RQn, avg_dict):\n",
    "    # Find relevant keys in selected_RQs that match RQn pattern\n",
    "    RQn_keys = [q for q in avg_dict.keys() if q.startswith(RQn + '_')]\n",
    "    \n",
    "    # Get corresponding values, only if key exists\n",
    "    RQn_values = {q: avg_dict[q] for q in RQn_keys if q in avg_dict}\n",
    "    \n",
    "    return RQn_values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS_mu = deepseek_chat_res.mean()\n",
    "OA_mu = openai_gpt4o_res.mean()\n",
    "MT_mu = mistral_res.mean()\n",
    "GR_mu = xai_grok_res.mean()\n",
    "\n",
    "DS_sg = deepseek_chat_res.sem()\n",
    "OA_sg = openai_gpt4o_res.sem()\n",
    "MT_sg = mistral_res.sem()\n",
    "GR_sg = xai_grok_res.sem()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define default colors and alpha values\n",
    "default_colors = {\n",
    "    \"DeepSeek Chat\": \"darksalmon\",\n",
    "    \"OpenAI GPT-4o\": \"peachpuff\",\n",
    "    \"Mistral AI\": \"slateblue\",\n",
    "    \"xAI Grok\": \"slategrey\"\n",
    "}\n",
    "\n",
    "default_alpha = {\n",
    "    \"DeepSeek Chat\": 0.8,\n",
    "    \"OpenAI GPT-4o\": 0.8,\n",
    "    \"Mistral AI\": 0.75,\n",
    "    \"xAI Grok\": 0.85\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countries = [\"USA\", \"China\", \"HK\", \"Taiwan\", \"Russia\", \"Japan\", \"UN\"]\n",
    "countries = [\"Japan\", \"Taiwan\", \"USA\", \"UN\", \"HK\", \"China\", \"Russia\",][::-1]\n",
    "qs = [\"RQ1_{}\".format(i) for i in countries]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "w = 0.2\n",
    "\n",
    "plt.bar(np.arange(len(qs))-1.5*w, DS_mu[qs], width=w, color =  \"darksalmon\", label = \"DeepSeek Chat\", \n",
    "        edgecolor='black', linewidth=1)\n",
    "plt.bar(np.arange(len(qs))-0.5*w, OA_mu[qs], width=w, color =  \"peachpuff\",  label = \"OpenAI GPT-4o\", \n",
    "        edgecolor='black', linewidth=1, hatch=\"/\")\n",
    "plt.bar(np.arange(len(qs))+0.5*w, MT_mu[qs], width=w, color =  \"slateblue\",  label = \"Mistral AI\" , \n",
    "        edgecolor='black', linewidth=1, hatch=\"-\")\n",
    "plt.bar(np.arange(len(qs))+1.5*w, GR_mu[qs], width=w, color =  \"slategrey\",  label = \"xAI Grok\" , \n",
    "        edgecolor='black', linewidth=1, hatch=\".\")\n",
    "\n",
    "plt.errorbar(np.arange(len(qs))-1.5*w, DS_mu[qs], yerr = DS_sg[qs]*3,linestyle='none',color=\"k\")\n",
    "plt.errorbar(np.arange(len(qs))-0.5*w, OA_mu[qs], yerr = OA_sg[qs]*3,linestyle='none',color=\"k\")\n",
    "plt.errorbar(np.arange(len(qs))+0.5*w, MT_mu[qs], yerr = MT_sg[qs]*3,linestyle='none',color=\"k\")\n",
    "plt.errorbar(np.arange(len(qs))+1.5*w, GR_mu[qs], yerr = GR_sg[qs]*3,linestyle='none',color=\"k\")\n",
    "\n",
    "plt.ylim(0,5)\n",
    "\n",
    "plt.xticks(np.arange(len(qs)), countries, fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.ylabel(\"Favorability\", fontsize=24)\n",
    "\n",
    "plt.title(\"a) Four LLM models and favorability scores\", fontsize=20)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "\n",
    "plt.savefig(\"figures/countries.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['RQ4_Xi', 'RQ5_Xi', 'RQ6_Xi', 'RQ7_Xi', 'RQ8_Xi', 'RQ9_Xi', 'RQ4_Biden',\\n       'RQ5_Biden', 'RQ6_Biden', 'RQ7_Biden', 'RQ8_Biden', 'RQ9_Biden',\\n       'RQ4_Trump', 'RQ5_Trump', 'RQ6_Trump', 'RQ7_Trump', 'RQ8_Trump',\\n       'RQ9_Trump', 'RQ4_Putin', 'RQ5_Putin', 'RQ6_Putin', 'RQ7_Putin',\\n       'RQ8_Putin', 'RQ9_Putin', 'RQ4_Zelenskyy', 'RQ5_Zelenskyy',\\n       'RQ6_Zelenskyy', 'RQ7_Zelenskyy', 'RQ8_Zelenskyy', 'RQ9_Zelenskyy',\\n       'RQ4_Macron', 'RQ5_Macron', 'RQ6_Macron', 'RQ7_Macron', 'RQ8_Macron',\\n       'RQ9_Macron'],\\n      dtype='object')] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m10\u001b[39m)) :\n\u001b[1;32m      5\u001b[0m         QS\u001b[38;5;241m.\u001b[39mappend( \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRQ\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(i,p) )\n\u001b[0;32m----> 7\u001b[0m A \u001b[38;5;241m=\u001b[39m \u001b[43mDS_mu\u001b[49m\u001b[43m[\u001b[49m\u001b[43mQS\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[1;32m      8\u001b[0m A[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mllm\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDeepSeek\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     10\u001b[0m B \u001b[38;5;241m=\u001b[39m MT_mu[QS]\u001b[38;5;241m.\u001b[39mreset_index()\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:1033\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     key \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(key, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m)\n\u001b[1;32m   1031\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_values(key)\n\u001b[0;32m-> 1033\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_with\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:1073\u001b[0m, in \u001b[0;36mSeries._get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[key]\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;66;03m# handle the dup indexing case GH#4246\u001b[39;00m\n\u001b[0;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1103\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1100\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1102\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1332\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1329\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   1330\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index with multidimensional key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1332\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1334\u001b[0m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n\u001b[1;32m   1335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_nested_tuple(key, labels):\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1272\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1269\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m   1271\u001b[0m \u001b[38;5;66;03m# A collection of keys\u001b[39;00m\n\u001b[0;32m-> 1272\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[1;32m   1274\u001b[0m     {axis: [keyarr, indexer]}, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1275\u001b[0m )\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1462\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1459\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m   1460\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[0;32m-> 1462\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1464\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:5877\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   5874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   5875\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 5877\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5879\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m   5880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m   5881\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:5938\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   5936\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[1;32m   5937\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 5938\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   5940\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m   5941\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['RQ4_Xi', 'RQ5_Xi', 'RQ6_Xi', 'RQ7_Xi', 'RQ8_Xi', 'RQ9_Xi', 'RQ4_Biden',\\n       'RQ5_Biden', 'RQ6_Biden', 'RQ7_Biden', 'RQ8_Biden', 'RQ9_Biden',\\n       'RQ4_Trump', 'RQ5_Trump', 'RQ6_Trump', 'RQ7_Trump', 'RQ8_Trump',\\n       'RQ9_Trump', 'RQ4_Putin', 'RQ5_Putin', 'RQ6_Putin', 'RQ7_Putin',\\n       'RQ8_Putin', 'RQ9_Putin', 'RQ4_Zelenskyy', 'RQ5_Zelenskyy',\\n       'RQ6_Zelenskyy', 'RQ7_Zelenskyy', 'RQ8_Zelenskyy', 'RQ9_Zelenskyy',\\n       'RQ4_Macron', 'RQ5_Macron', 'RQ6_Macron', 'RQ7_Macron', 'RQ8_Macron',\\n       'RQ9_Macron'],\\n      dtype='object')] are in the [index]\""
     ]
    }
   ],
   "source": [
    "QS = []\n",
    "\n",
    "for p in people:\n",
    "    for i in list(range(4,10)) :\n",
    "        QS.append( \"RQ{}_{}\".format(i,p) )\n",
    "\n",
    "A = DS_mu[QS].reset_index()\n",
    "A[\"llm\"] = \"DeepSeek\"\n",
    "\n",
    "B = MT_mu[QS].reset_index()\n",
    "B[\"llm\"] = \"Mistral\"\n",
    "A = pd.concat((A,B))\n",
    "\n",
    "B = OA_mu[QS].reset_index()\n",
    "B[\"llm\"] = \"OpenAI\"\n",
    "A = pd.concat((A,B))\n",
    "\n",
    "B = GR_mu[QS].reset_index()\n",
    "B[\"llm\"] = \"Grok\"\n",
    "A = pd.concat((A,B))\n",
    "\n",
    "A.columns = [\"Question\", \"mu\", \"llm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'people' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m QS \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m \u001b[43mpeople\u001b[49m:\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m10\u001b[39m)) :\n\u001b[1;32m      5\u001b[0m         QS\u001b[38;5;241m.\u001b[39mappend( \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRQ\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(i,p) )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined"
     ]
    }
   ],
   "source": [
    "QS = []\n",
    "\n",
    "for p in people:\n",
    "    for i in list(range(4,10)) :\n",
    "        QS.append( \"RQ{}_{}\".format(i,p) )\n",
    "\n",
    "C = DS_sg[QS].reset_index()\n",
    "C[\"llm\"] = \"DeepSeek\"\n",
    "\n",
    "B = MT_sg[QS].reset_index()\n",
    "B[\"llm\"] = \"Mistral\"\n",
    "C = pd.concat((C,B))\n",
    "\n",
    "B = OA_sg[QS].reset_index()\n",
    "B[\"llm\"] = \"OpenAI\"\n",
    "C = pd.concat((C,B))\n",
    "\n",
    "B = GR_sg[QS].reset_index()\n",
    "B[\"llm\"] = \"Grok\"\n",
    "C = pd.concat((C,B))\n",
    "\n",
    "C.columns = [\"Question\", \"sg\", \"llm\"]\n",
    "\n",
    "A[\"sg\"] = C[\"sg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "q2issue = {\"RQ4\" : \"Domestic Policy\", \n",
    "           \"RQ5\" : \"International Relations\", \n",
    "           \"RQ6\" : \"Economy\", \n",
    "           \"RQ7\" : \"Global Influence\",\n",
    "           \"RQ8\" : \"Human Rights\",\n",
    "           \"RQ9\" : \"Environment\"}\n",
    "\n",
    "# A[\"policy\"] = A.Question.apply(lambda x: q2issue[ x.split(\"_\")[0] ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B = A[A.Question.str.contains(\"RQ4\")]\n",
    "people = [\"Xi\", \"Biden\", \"Trump\", \"Putin\", \"Zelenskyy\", \"Macron\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RQ4_Xi_Jinping',\n",
       " 'RQ4_Joe_Biden',\n",
       " 'RQ4_Donald_Trump',\n",
       " 'RQ4_Vladimir_Putin',\n",
       " 'RQ4_Volodymyr_Zelenskyy',\n",
       " 'RQ4_Emmanuel_Macron']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qs = [q for q in DS_mu.index if \"RQ4\" in q]\n",
    "qs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdfa02d3280>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 0.1\n",
    "\n",
    "plt.figure(figsize=(24,20))\n",
    "\n",
    "level = [\"a\",\"b\",\"c\",\"d\"]\n",
    "\n",
    "for j,i in enumerate([4,5,8,9]):\n",
    "    \n",
    "    qs = [q for q in DS_mu.index if \"RQ{}\".format(i) in q]\n",
    "\n",
    "    plt.subplot(2,2,j+1)\n",
    "    \n",
    "    plt.title(\"{}) \".format(level[j]) + q2issue[f\"RQ{i}\"], fontsize=28)\n",
    "\n",
    "    plt.scatter(DS_mu[qs], np.arange(len(qs))-1.5*w, color =  \"darksalmon\", label = \"DeepSeek Chat\", \n",
    "                s=300, marker=\"o\", edgecolors='black',)\n",
    "    plt.scatter(OA_mu[qs], np.arange(len(qs))-0.5*w, color =  \"peachpuff\",  label = \"OpenAI GPT-4o\", \n",
    "                s=300, marker=\"^\", edgecolors='black',)\n",
    "    plt.scatter(MT_mu[qs], np.arange(len(qs))+0.5*w, color =  \"slateblue\",  label = \"Mistral AI\", \n",
    "                s=300, marker=\"v\", edgecolors='black',)\n",
    "    plt.scatter(GR_mu[qs], np.arange(len(qs))+1.5*w, color =  \"slategrey\",  label = \"xAI Grok\", \n",
    "                s=300, marker=\"s\", edgecolors='black',)\n",
    "\n",
    "    plt.errorbar(DS_mu[qs], np.arange(len(qs))-1.5*w, xerr = DS_sg[qs]*3,linestyle='none',\n",
    "                 color=\"k\", capsize=5, zorder=5)\n",
    "    plt.errorbar(OA_mu[qs], np.arange(len(qs))-0.5*w, xerr = OA_sg[qs]*3,linestyle='none',\n",
    "                 color=\"k\", capsize=5, zorder=5)\n",
    "    plt.errorbar(MT_mu[qs], np.arange(len(qs))+0.5*w, xerr = MT_sg[qs]*3,linestyle='none',\n",
    "                 color=\"k\", capsize=5, zorder=5)\n",
    "    plt.errorbar(GR_mu[qs], np.arange(len(qs))+1.5*w, xerr = GR_sg[qs]*3,linestyle='none',\n",
    "                 color=\"k\", capsize=5, zorder=5)\n",
    "\n",
    "    plt.vlines(3,0,5, linestyle=\"--\", color=\"red\")\n",
    "    \n",
    "    if i in [8,9]:\n",
    "        plt.xlabel(\"Favorability\", fontsize=30)\n",
    "#     else:\n",
    "#         plt.xticks([1,2,3,4,5], [\"1\",\"2\", \n",
    "#                          \"Neutral\", \"4\",\"5\",], fontsize=24)\n",
    "        plt.xticks([1,2,3,4,5], [\"Very\\nUnfav(1)\",\"Unfavorable\\n(2)\", \n",
    "                         \"Neutral\\n(3)\", \"Favorable\\n(4)\",\"Very\\nFav(5)\",], fontsize=24)   \n",
    "    else:\n",
    "        plt.xticks([])\n",
    "    plt.yticks(np.arange(len(qs)), people)\n",
    "    plt.yticks(fontsize=24)\n",
    "    \n",
    "\n",
    "\n",
    "    plt.xlim(0.9,5)\n",
    "    \n",
    "    plt.legend(fontsize=24)\n",
    "\n",
    "    plt.hlines(np.arange(5) + 0.5, 1.3, 4.65, linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "plt.legend(loc='lower center',\n",
    "           ncol=4,  \n",
    "           bbox_to_anchor=(0.5, -0.02))    \n",
    "\n",
    "# plt.savefig(\"figures/leader_policy_grid.png\", bbox_inches=\"tight\")\n",
    "# plt.savefig(\"figures/leader_policy_grid_EB.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 0.1\n",
    "plt.figure(figsize=(24, 20))\n",
    "\n",
    "level = [\"a\", \"b\", \"c\", \"d\"]\n",
    "\n",
    "ax_for_legend = None\n",
    "handles = labels = None  # will fill from the first subplot we draw\n",
    "\n",
    "for j, i in enumerate([4, 5, 8, 9]):\n",
    "    qs = [q for q in DS_mu.index if \"RQ{}\".format(i) in q]\n",
    "\n",
    "    plt.subplot(2, 2, j + 1)\n",
    "    ax = plt.gca()\n",
    "    if ax_for_legend is None:\n",
    "        ax_for_legend = ax  # remember one axes to “own” the shared legend\n",
    "\n",
    "    plt.title(\"{}) \".format(level[j]) + q2issue[f\"RQ{i}\"], fontsize=28)\n",
    "\n",
    "    plt.scatter(DS_mu[qs], np.arange(len(qs)) - 1.5 * w, color=\"darksalmon\",\n",
    "                label=\"DeepSeek Chat\", s=300, marker=\"o\", edgecolors='black')\n",
    "    plt.scatter(OA_mu[qs], np.arange(len(qs)) - 0.5 * w, color=\"peachpuff\",\n",
    "                label=\"OpenAI GPT-4o\", s=300, marker=\"^\", edgecolors='black')\n",
    "    plt.scatter(MT_mu[qs], np.arange(len(qs)) + 0.5 * w, color=\"slateblue\",\n",
    "                label=\"Mistral AI\", s=300, marker=\"v\", edgecolors='black')\n",
    "    plt.scatter(GR_mu[qs], np.arange(len(qs)) + 1.5 * w, color=\"slategrey\",\n",
    "                label=\"xAI Grok\", s=300, marker=\"s\", edgecolors='black')\n",
    "\n",
    "    plt.vlines(3, 0, 5, linestyle=\"--\", color=\"red\")\n",
    "\n",
    "    if i in [8, 9]:\n",
    "        plt.xlabel(\"Favorability\", fontsize=30)\n",
    "        plt.xticks([1, 2, 3, 4, 5],\n",
    "                   [\"Very\\nUnfav(1)\", \"Unfavorable\\n(2)\", \"Neutral\\n(3)\", \"Favorable\\n(4)\", \"Very\\nFav(5)\"],\n",
    "                   fontsize=24)\n",
    "    else:\n",
    "        plt.xticks([])\n",
    "\n",
    "    plt.yticks(np.arange(len(qs)), people, fontsize=24)\n",
    "    plt.xlim(0.9, 5)\n",
    "    plt.hlines(np.arange(5) + 0.5, 1.3, 4.65, linestyle=\"--\", alpha=0.3)\n",
    "\n",
    "    # Grab legend entries from the first subplot only (all subplots share the same series)\n",
    "    if handles is None or labels is None:\n",
    "        handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "# --- single shared legend using plt.legend (attached to one axes) ---\n",
    "plt.subplots_adjust(bottom=0.16)  # make room for the legend\n",
    "plt.sca(ax_for_legend)\n",
    "\n",
    "plt.legend(handles, labels,\n",
    "           loc=\"upper center\",\n",
    "           bbox_to_anchor=(1, -1.4),  # push below all subplots\n",
    "           ncol=len(labels),\n",
    "           frameon=True,\n",
    "           fontsize=24)\n",
    "\n",
    "plt.savefig(\"figures/leader_policy_grid.png\", bbox_inches=\"tight\")\n",
    "# plt.savefig(\"figures/leader_policy_grid_EB.png\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "political_RQs = ['RQ1_USA',\n",
    " 'RQ2_USA',\n",
    " 'RQ3_USA',\n",
    " 'RQ1_China',\n",
    " 'RQ2_China',\n",
    " 'RQ3_China',\n",
    " 'RQ1_HK',\n",
    " 'RQ2_HK',\n",
    " 'RQ3_HK',\n",
    " 'RQ1_Taiwan',\n",
    " 'RQ2_Taiwan',\n",
    " 'RQ3_Taiwan',\n",
    " 'RQ1_Russia',\n",
    " 'RQ2_Russia',\n",
    " 'RQ3_Russia',\n",
    " 'RQ1_Japan',\n",
    " 'RQ2_Japan',\n",
    " 'RQ3_Japan',\n",
    " 'RQ1_France',\n",
    " 'RQ2_France',\n",
    " 'RQ3_France',\n",
    " 'RQ1_EU',\n",
    " 'RQ2_EU',\n",
    " 'RQ3_EU',\n",
    " 'RQ1_UN',\n",
    " 'RQ2_UN',\n",
    " 'RQ3_UN',\n",
    " 'RQ1_NATO',\n",
    " 'RQ2_NATO',\n",
    " 'RQ3_NATO',\n",
    " 'RQ4_Xi_Jinping',\n",
    " 'RQ5_Xi_Jinping',\n",
    " 'RQ6_Xi_Jinping',\n",
    " 'RQ7_Xi_Jinping',\n",
    " 'RQ8_Xi_Jinping',\n",
    " 'RQ9_Xi_Jinping',\n",
    " 'RQ4_Joe_Biden',\n",
    " 'RQ5_Joe_Biden',\n",
    " 'RQ6_Joe_Biden',\n",
    " 'RQ7_Joe_Biden',\n",
    " 'RQ8_Joe_Biden',\n",
    " 'RQ9_Joe_Biden',\n",
    " 'RQ4_Donald_Trump',\n",
    " 'RQ5_Donald_Trump',\n",
    " 'RQ6_Donald_Trump',\n",
    " 'RQ7_Donald_Trump',\n",
    " 'RQ8_Donald_Trump',\n",
    " 'RQ9_Donald_Trump',\n",
    " 'RQ4_Vladimir_Putin',\n",
    " 'RQ5_Vladimir_Putin',\n",
    " 'RQ6_Vladimir_Putin',\n",
    " 'RQ7_Vladimir_Putin',\n",
    " 'RQ8_Vladimir_Putin',\n",
    " 'RQ9_Vladimir_Putin',\n",
    " 'RQ4_Volodymyr_Zelenskyy',\n",
    " 'RQ5_Volodymyr_Zelenskyy',\n",
    " 'RQ6_Volodymyr_Zelenskyy',\n",
    " 'RQ7_Volodymyr_Zelenskyy',\n",
    " 'RQ8_Volodymyr_Zelenskyy',\n",
    " 'RQ9_Volodymyr_Zelenskyy',\n",
    " 'RQ4_Emmanuel_Macron',\n",
    " 'RQ5_Emmanuel_Macron',\n",
    " 'RQ6_Emmanuel_Macron',\n",
    " 'RQ7_Emmanuel_Macron',\n",
    " 'RQ8_Emmanuel_Macron',\n",
    " 'RQ9_Emmanuel_Macron']\n",
    "\n",
    "tech_comp_RQs = ['RQ10_Microsoft',\n",
    " 'RQ10_Google',\n",
    " 'RQ10_Apple',\n",
    " 'RQ10_Baidu',\n",
    " 'RQ10_ByteDance',\n",
    " 'RQ10_Disney',\n",
    " 'RQ10_Nvidia',\n",
    " 'RQ10_Meta',\n",
    " 'RQ10_Tesla',\n",
    " 'RQ10_SpaceX',\n",
    " 'RQ10_xAI',\n",
    " 'RQ10_Tencent',\n",
    " 'RQ10_Amazon',\n",
    " 'RQ10_IBM',\n",
    " 'RQ10_Samsung',\n",
    " 'RQ10_Netflix']\n",
    "\n",
    "LLM_RQs = ['RQ11_ChatGPT',\n",
    " 'RQ11_DeepSeek',\n",
    " 'RQ11_Mistral_AI',\n",
    " 'RQ11_Gemini',\n",
    " 'RQ11_LLaMA',\n",
    " 'RQ11_Grok',\n",
    " 'RQ11_Le_Chat',\n",
    " 'RQ11_Claude',\n",
    " 'RQ11_Perplexity_AI']\n",
    "\n",
    "media_RQs = ['RQ12_Twitter',\n",
    " 'RQ12_LinkedIn',\n",
    " 'RQ12_InCareer',\n",
    " 'RQ12_TikTok',\n",
    " 'RQ12_Instagram',\n",
    " 'RQ12_Facebook',\n",
    " 'RQ12_Reddit',\n",
    " 'RQ12_Threads',\n",
    " 'RQ12_YouTube',\n",
    " 'RQ12_WeChat',\n",
    " 'RQ12_Telegram']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
